Research On Flexible Job-shop Scheduling Problem Based On Biogeography-based Optimization Algorithm

Because of the personalization and timeliness of customer demand, the production mode of manufacturing enterprises has developed from few-varieties and large-batch to multi-varieties, small-batch and more flexible. The research achievements about traditional job-shop scheduling can not effectively solve the problem which is derived by new manufacturing mode. As an extension of classical job-shop scheduling problem, flexible job-shop scheduling problem is more consistent with actual production and increase the flexibility of scheduling. Therefore, study on flexible job shop scheduling problem has important theoretical significance and practical application value.Based on the description of the flexible job-shop scheduling problem, this paper analyzed the constraint conditions and objective functions which the problem need to satisfy, and the mathematical model of this problem was established. Then we studied on a swarm intelligence algorithm proposed in recent years, namely biogeography-based optimization algorithm. And the two basic operations of the algorithm which were called migration and mutation were systematically analyzed. The algorithm was also improved in terms of two aspects:migration rate and mutation rate. According to the characteristics of the flexible job shop scheduling problem, an improved biogeography-based optimization algorithm was proposed in this paper. The program used a combination of the machine-based and order-based coding mechanism, and superior individuals were generated based on heuristic rules in the initial population. Migration and mutation mechanism was improved based on standard biogeography-based optimization algorithm, in line with the scheduling problem of mobility model and adaptive mutation mechanism, for overcoming the shortcoming of premature and slow convergence of traditional algorithms. Through simulation and comparison experiments, the results demonstrated that the presented algorithm can effectively solve the flexible job-shop scheduling problem, and had better search ability and robustness compared with other traditional algorithm.